Overview

Dataset statistics

Number of variables28
Number of observations120
Missing cells802
Missing cells (%)23.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.7 KiB
Average record size in memory228.0 B

Variable types

Categorical5
Numeric23

Alerts

Financial Year is highly correlated with Risk_boolHigh correlation
Assets is highly correlated with Goodwill and 14 other fieldsHigh correlation
Goodwill is highly correlated with Assets and 7 other fieldsHigh correlation
Intangible Assets Other Than Goodwill is highly correlated with Assets and 6 other fieldsHigh correlation
Liabilities is highly correlated with Assets and 12 other fieldsHigh correlation
Cash And Bank Balances is highly correlated with Assets and 5 other fieldsHigh correlation
Trade And Other Receivables Current is highly correlated with Assets and 8 other fieldsHigh correlation
Current Assets is highly correlated with Assets and 10 other fieldsHigh correlation
Current Liabilities is highly correlated with Assets and 10 other fieldsHigh correlation
Other Current Financial Assets is highly correlated with Assets and 10 other fieldsHigh correlation
Other Noncurrent Financial Assets is highly correlated with Assets and 9 other fieldsHigh correlation
Other Current Financial Liabilities is highly correlated with Other Current Financial Assets and 2 other fieldsHigh correlation
Other Noncurrent Financial Liabilities is highly correlated with Assets and 13 other fieldsHigh correlation
Cash Flows From Used In Operating Activities is highly correlated with Intangible Assets Other Than Goodwill and 5 other fieldsHigh correlation
Current Loans And Borrowings is highly correlated with Other Noncurrent Financial Assets and 2 other fieldsHigh correlation
Noncurrent Loans And Borrowings is highly correlated with Assets and 5 other fieldsHigh correlation
Profit Loss is highly correlated with Assets and 13 other fieldsHigh correlation
Profit Loss Before Taxation is highly correlated with Assets and 13 other fieldsHigh correlation
Finance Costs is highly correlated with Assets and 4 other fieldsHigh correlation
Revenue is highly correlated with Assets and 8 other fieldsHigh correlation
Net Tangible Assets margin is highly correlated with Quick RatioHigh correlation
Operating cash flow rati is highly correlated with Other Current Financial Assets and 1 other fieldsHigh correlation
Debt ratio is highly correlated with Goodwill and 1 other fieldsHigh correlation
NPBT is highly correlated with Profit Loss and 2 other fieldsHigh correlation
Quick Ratio is highly correlated with Net Tangible Assets marginHigh correlation
Interest coverage ratio is highly correlated with Other Noncurrent Financial Liabilities and 3 other fieldsHigh correlation
Risk_bool is highly correlated with Financial YearHigh correlation
Financial Year is highly correlated with Risk_boolHigh correlation
Assets is highly correlated with Goodwill and 12 other fieldsHigh correlation
Goodwill is highly correlated with Assets and 4 other fieldsHigh correlation
Intangible Assets Other Than Goodwill is highly correlated with Assets and 9 other fieldsHigh correlation
Liabilities is highly correlated with Intangible Assets Other Than Goodwill and 13 other fieldsHigh correlation
Cash And Bank Balances is highly correlated with Assets and 13 other fieldsHigh correlation
Trade And Other Receivables Current is highly correlated with Liabilities and 6 other fieldsHigh correlation
Current Assets is highly correlated with Assets and 14 other fieldsHigh correlation
Current Liabilities is highly correlated with Intangible Assets Other Than Goodwill and 13 other fieldsHigh correlation
Other Current Financial Assets is highly correlated with Assets and 13 other fieldsHigh correlation
Other Noncurrent Financial Assets is highly correlated with Assets and 13 other fieldsHigh correlation
Other Current Financial Liabilities is highly correlated with Assets and 11 other fieldsHigh correlation
Other Noncurrent Financial Liabilities is highly correlated with Assets and 15 other fieldsHigh correlation
Cash Flows From Used In Operating Activities is highly correlated with Intangible Assets Other Than Goodwill and 10 other fieldsHigh correlation
Current Loans And Borrowings is highly correlated with Liabilities and 6 other fieldsHigh correlation
Noncurrent Loans And Borrowings is highly correlated with Assets and 14 other fieldsHigh correlation
Profit Loss is highly correlated with Assets and 7 other fieldsHigh correlation
Profit Loss Before Taxation is highly correlated with Assets and 8 other fieldsHigh correlation
Finance Costs is highly correlated with Assets and 10 other fieldsHigh correlation
Revenue is highly correlated with Liabilities and 8 other fieldsHigh correlation
Debt ratio is highly correlated with Other Noncurrent Financial LiabilitiesHigh correlation
NPBT is highly correlated with Assets and 6 other fieldsHigh correlation
Risk_bool is highly correlated with Financial YearHigh correlation
Financial Year is highly correlated with Risk_boolHigh correlation
Assets is highly correlated with Liabilities and 8 other fieldsHigh correlation
Goodwill is highly correlated with Intangible Assets Other Than Goodwill and 4 other fieldsHigh correlation
Intangible Assets Other Than Goodwill is highly correlated with Goodwill and 2 other fieldsHigh correlation
Liabilities is highly correlated with Assets and 8 other fieldsHigh correlation
Trade And Other Receivables Current is highly correlated with Assets and 5 other fieldsHigh correlation
Current Assets is highly correlated with Assets and 5 other fieldsHigh correlation
Current Liabilities is highly correlated with Assets and 5 other fieldsHigh correlation
Other Current Financial Assets is highly correlated with Intangible Assets Other Than Goodwill and 3 other fieldsHigh correlation
Other Noncurrent Financial Assets is highly correlated with Assets and 8 other fieldsHigh correlation
Other Current Financial Liabilities is highly correlated with Other Current Financial Assets and 2 other fieldsHigh correlation
Other Noncurrent Financial Liabilities is highly correlated with Assets and 11 other fieldsHigh correlation
Cash Flows From Used In Operating Activities is highly correlated with Other Noncurrent Financial Liabilities and 1 other fieldsHigh correlation
Current Loans And Borrowings is highly correlated with Other Noncurrent Financial Assets and 1 other fieldsHigh correlation
Noncurrent Loans And Borrowings is highly correlated with Liabilities and 4 other fieldsHigh correlation
Profit Loss is highly correlated with Assets and 5 other fieldsHigh correlation
Profit Loss Before Taxation is highly correlated with Assets and 6 other fieldsHigh correlation
Finance Costs is highly correlated with Liabilities and 2 other fieldsHigh correlation
Revenue is highly correlated with Assets and 7 other fieldsHigh correlation
Net Tangible Assets margin is highly correlated with Quick RatioHigh correlation
Operating cash flow rati is highly correlated with Other Current Financial Assets and 1 other fieldsHigh correlation
Debt ratio is highly correlated with GoodwillHigh correlation
NPBT is highly correlated with Profit Loss and 1 other fieldsHigh correlation
Quick Ratio is highly correlated with Net Tangible Assets marginHigh correlation
Risk_bool is highly correlated with Financial YearHigh correlation
Goodwill is highly correlated with Other Noncurrent Financial LiabilitiesHigh correlation
Risk_bool is highly correlated with Financial YearHigh correlation
Financial Year is highly correlated with Risk_boolHigh correlation
Other Noncurrent Financial Liabilities is highly correlated with GoodwillHigh correlation
Financial Year is highly correlated with Current Loans And Borrowings and 2 other fieldsHigh correlation
Description Of Presentation Currency is highly correlated with Assets and 8 other fieldsHigh correlation
Assets is highly correlated with Description Of Presentation Currency and 16 other fieldsHigh correlation
Goodwill is highly correlated with Trade And Other Receivables Current and 1 other fieldsHigh correlation
Intangible Assets Other Than Goodwill is highly correlated with Assets and 9 other fieldsHigh correlation
Liabilities is highly correlated with Description Of Presentation Currency and 15 other fieldsHigh correlation
Cash And Bank Balances is highly correlated with Description Of Presentation Currency and 17 other fieldsHigh correlation
Trade And Other Receivables Current is highly correlated with Description Of Presentation Currency and 16 other fieldsHigh correlation
Current Assets is highly correlated with Description Of Presentation Currency and 17 other fieldsHigh correlation
Current Liabilities is highly correlated with Description Of Presentation Currency and 16 other fieldsHigh correlation
Other Current Financial Assets is highly correlated with Operating cash flow ratiHigh correlation
Other Noncurrent Financial Assets is highly correlated with Cash And Bank Balances and 3 other fieldsHigh correlation
Other Current Financial Liabilities is highly correlated with Assets and 10 other fieldsHigh correlation
Other Noncurrent Financial Liabilities is highly correlated with Description Of Presentation Currency and 12 other fieldsHigh correlation
Cash Flows From Used In Operating Activities is highly correlated with Description Of Presentation Currency and 16 other fieldsHigh correlation
Current Loans And Borrowings is highly correlated with Financial Year and 14 other fieldsHigh correlation
Noncurrent Loans And Borrowings is highly correlated with Assets and 12 other fieldsHigh correlation
Profit Loss is highly correlated with Assets and 14 other fieldsHigh correlation
Profit Loss Before Taxation is highly correlated with Assets and 14 other fieldsHigh correlation
Finance Costs is highly correlated with Assets and 16 other fieldsHigh correlation
Revenue is highly correlated with Description Of Presentation Currency and 14 other fieldsHigh correlation
Net Tangible Assets margin is highly correlated with Financial Year and 4 other fieldsHigh correlation
Operating cash flow rati is highly correlated with Goodwill and 5 other fieldsHigh correlation
Debt ratio is highly correlated with Current Loans And BorrowingsHigh correlation
NPBT is highly correlated with Assets and 11 other fieldsHigh correlation
Quick Ratio is highly correlated with Operating cash flow rati and 1 other fieldsHigh correlation
Risk_bool is highly correlated with Financial Year and 2 other fieldsHigh correlation
Goodwill has 114 (95.0%) missing values Missing
Intangible Assets Other Than Goodwill has 76 (63.3%) missing values Missing
Cash And Bank Balances has 6 (5.0%) missing values Missing
Trade And Other Receivables Current has 6 (5.0%) missing values Missing
Other Current Financial Assets has 112 (93.3%) missing values Missing
Other Noncurrent Financial Assets has 110 (91.7%) missing values Missing
Other Current Financial Liabilities has 83 (69.2%) missing values Missing
Other Noncurrent Financial Liabilities has 113 (94.2%) missing values Missing
Current Loans And Borrowings has 70 (58.3%) missing values Missing
Noncurrent Loans And Borrowings has 82 (68.3%) missing values Missing
Profit Loss has 5 (4.2%) missing values Missing
Finance Costs has 25 (20.8%) missing values Missing
Assets has unique values Unique
Liabilities has unique values Unique
Current Assets has unique values Unique
Current Liabilities has unique values Unique
Cash Flows From Used In Operating Activities has unique values Unique
Profit Loss Before Taxation has unique values Unique
Revenue has unique values Unique
Net Tangible Assets margin has unique values Unique
Operating cash flow rati has unique values Unique
NPBT has unique values Unique
Intangible Assets Other Than Goodwill has 5 (4.2%) zeros Zeros
Other Current Financial Assets has 2 (1.7%) zeros Zeros
Other Noncurrent Financial Assets has 2 (1.7%) zeros Zeros
Other Current Financial Liabilities has 7 (5.8%) zeros Zeros
Current Loans And Borrowings has 6 (5.0%) zeros Zeros
Noncurrent Loans And Borrowings has 7 (5.8%) zeros Zeros
Debt ratio has 48 (40.0%) zeros Zeros
Quick Ratio has 5 (4.2%) zeros Zeros
Interest coverage ratio has 34 (28.3%) zeros Zeros

Reproduction

Analysis started2022-10-06 12:29:32.777255
Analysis finished2022-10-06 12:30:29.478390
Duration56.7 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Financial Year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2019
103 
2018
11 
2017
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters480
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019103
85.8%
201811
 
9.2%
20176
 
5.0%

Length

2022-10-06T20:30:29.545023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T20:30:29.654372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2019103
85.8%
201811
 
9.2%
20176
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2120
25.0%
0120
25.0%
1120
25.0%
9103
21.5%
811
 
2.3%
76
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number480
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2120
25.0%
0120
25.0%
1120
25.0%
9103
21.5%
811
 
2.3%
76
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2120
25.0%
0120
25.0%
1120
25.0%
9103
21.5%
811
 
2.3%
76
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2120
25.0%
0120
25.0%
1120
25.0%
9103
21.5%
811
 
2.3%
76
 
1.2%

Description Of Presentation Currency
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
SGD
71 
USD
47 
JPY
 
1
EUR
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters360
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st rowSGD
2nd rowSGD
3rd rowSGD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
SGD71
59.2%
USD47
39.2%
JPY1
 
0.8%
EUR1
 
0.8%

Length

2022-10-06T20:30:29.748135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T20:30:29.841864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
sgd71
59.2%
usd47
39.2%
jpy1
 
0.8%
eur1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
S118
32.8%
D118
32.8%
G71
19.7%
U48
13.3%
J1
 
0.3%
P1
 
0.3%
Y1
 
0.3%
E1
 
0.3%
R1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter360
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S118
32.8%
D118
32.8%
G71
19.7%
U48
13.3%
J1
 
0.3%
P1
 
0.3%
Y1
 
0.3%
E1
 
0.3%
R1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin360
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S118
32.8%
D118
32.8%
G71
19.7%
U48
13.3%
J1
 
0.3%
P1
 
0.3%
Y1
 
0.3%
E1
 
0.3%
R1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S118
32.8%
D118
32.8%
G71
19.7%
U48
13.3%
J1
 
0.3%
P1
 
0.3%
Y1
 
0.3%
E1
 
0.3%
R1
 
0.3%

Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01752005043
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:29.951212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.999698252 × 10-5
Q10.0003011615768
median0.001133801504
Q30.005244188836
95-th percentile0.03162158999
Maximum1
Range1
Interquartile range (IQR)0.004943027259

Descriptive statistics

Standard deviation0.09837879305
Coefficient of variation (CV)5.615211752
Kurtosis86.84523641
Mean0.01752005043
Median Absolute Deviation (MAD)0.001034702737
Skewness9.019229404
Sum2.102406051
Variance0.009678386922
MonotonicityNot monotonic
2022-10-06T20:30:30.076148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00370718081
 
0.8%
0.00044534138961
 
0.8%
0.39892297761
 
0.8%
0.005199490261
 
0.8%
0.00097086257891
 
0.8%
0.023090496381
 
0.8%
0.0011215088451
 
0.8%
9.403847802 × 10-51
 
0.8%
0.015662186811
 
0.8%
0.00053924718381
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
7.851339157 × 10-61
0.8%
1.431986799 × 10-51
0.8%
2.379639506 × 10-51
0.8%
2.87046917 × 10-51
0.8%
3.375384563 × 10-51
0.8%
4.032556868 × 10-51
0.8%
5.135223215 × 10-51
0.8%
8.36109321 × 10-51
0.8%
8.720073334 × 10-51
0.8%
ValueCountFrequency (%)
11
0.8%
0.39892297761
0.8%
0.12871867471
0.8%
0.053640952861
0.8%
0.049289115831
0.8%
0.04017425881
0.8%
0.031171449531
0.8%
0.026661296081
0.8%
0.025823322551
0.8%
0.024124033981
0.8%

Goodwill
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)66.7%
Missing114
Missing (%)95.0%
Memory size1.9 KiB
0.0
0.2556376729202198
1.0
0.4847163160886867

Length

Max length18
Median length3
Mean length8
Min length3

Characters and Unicode

Total characters48
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)50.0%

Sample

1st row0.2556376729202198
2nd row1.0
3rd row0.4847163160886867
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03
 
2.5%
0.25563767292021981
 
0.8%
1.01
 
0.8%
0.48471631608868671
 
0.8%
(Missing)114
95.0%

Length

2022-10-06T20:30:30.201120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T20:30:30.294881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03
50.0%
0.25563767292021981
 
16.7%
1.01
 
16.7%
0.48471631608868671
 
16.7%

Most occurring characters

ValueCountFrequency (%)
011
22.9%
.6
12.5%
66
12.5%
85
10.4%
24
 
8.3%
74
 
8.3%
14
 
8.3%
52
 
4.2%
32
 
4.2%
92
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42
87.5%
Other Punctuation6
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011
26.2%
66
14.3%
85
11.9%
24
 
9.5%
74
 
9.5%
14
 
9.5%
52
 
4.8%
32
 
4.8%
92
 
4.8%
42
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011
22.9%
.6
12.5%
66
12.5%
85
10.4%
24
 
8.3%
74
 
8.3%
14
 
8.3%
52
 
4.2%
32
 
4.2%
92
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011
22.9%
.6
12.5%
66
12.5%
85
10.4%
24
 
8.3%
74
 
8.3%
14
 
8.3%
52
 
4.2%
32
 
4.2%
92
 
4.2%

Intangible Assets Other Than Goodwill
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct40
Distinct (%)90.9%
Missing76
Missing (%)63.3%
Infinite0
Infinite (%)0.0%
Mean0.07911793617
Minimum0
Maximum1
Zeros5
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:30.416994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.000111981565
median0.001290394856
Q30.02667156061
95-th percentile0.4925972932
Maximum1
Range1
Interquartile range (IQR)0.02655957904

Descriptive statistics

Standard deviation0.2086853596
Coefficient of variation (CV)2.637649182
Kurtosis11.78057731
Mean0.07911793617
Median Absolute Deviation (MAD)0.001290394856
Skewness3.420624902
Sum3.481189192
Variance0.04354957931
MonotonicityNot monotonic
2022-10-06T20:30:30.535683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
05
 
4.2%
7.616559888 × 10-51
 
0.8%
0.05418638321
 
0.8%
0.20665540441
 
0.8%
0.0020961298721
 
0.8%
0.00010518106511
 
0.8%
0.0072716385331
 
0.8%
0.0028163136921
 
0.8%
0.0001892588191
 
0.8%
0.00018476686211
 
0.8%
Other values (30)30
 
25.0%
(Missing)76
63.3%
ValueCountFrequency (%)
05
4.2%
1.81346664 × 10-91
 
0.8%
3.224343686 × 10-61
 
0.8%
8.795313204 × 10-61
 
0.8%
7.25386656 × 10-51
 
0.8%
7.616559888 × 10-51
 
0.8%
0.00010518106511
 
0.8%
0.00011424839831
 
0.8%
0.00018476686211
 
0.8%
0.0001892588191
 
0.8%
ValueCountFrequency (%)
11
0.8%
0.81841749471
0.8%
0.53477317751
0.8%
0.25360061531
0.8%
0.24005319081
0.8%
0.20665540441
0.8%
0.088263234841
0.8%
0.086578531581
0.8%
0.084592778361
0.8%
0.05418638321
0.8%

Liabilities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02219910937
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:30.660323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0001300720061
Q10.0007048490547
median0.002312680818
Q30.01230416255
95-th percentile0.05146991055
Maximum1
Range1
Interquartile range (IQR)0.01159931349

Descriptive statistics

Standard deviation0.09756373812
Coefficient of variation (CV)4.394939296
Kurtosis86.92880222
Mean0.02219910937
Median Absolute Deviation (MAD)0.002140035629
Skewness8.89870668
Sum2.663893125
Variance0.009518682995
MonotonicityNot monotonic
2022-10-06T20:30:30.787967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0073364981091
 
0.8%
0.0010449309761
 
0.8%
11
 
0.8%
0.091812818251
 
0.8%
0.004748616041
 
0.8%
0.01703457091
 
0.8%
0.0046519314791
 
0.8%
0.00032657502321
 
0.8%
0.054833733561
 
0.8%
0.0020064310141
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
2.88625518 × 10-51
0.8%
6.237317801 × 10-51
0.8%
7.010823774 × 10-51
0.8%
9.613009485 × 10-51
0.8%
0.00012574138681
0.8%
0.00013029993341
0.8%
0.00013145725571
0.8%
0.00014584454051
0.8%
0.00014590250171
0.8%
ValueCountFrequency (%)
11
0.8%
0.31506056791
0.8%
0.20139854591
0.8%
0.13817157721
0.8%
0.091812818251
0.8%
0.054833733561
0.8%
0.051292867241
0.8%
0.050065575371
0.8%
0.045567962371
0.8%
0.043341233381
0.8%

Cash And Bank Balances
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.03254581778
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:30.916658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.773889113 × 10-5
Q10.0003440899737
median0.002030089405
Q30.009412580891
95-th percentile0.1568012185
Maximum1
Range1
Interquartile range (IQR)0.009068490917

Descriptive statistics

Standard deviation0.124901353
Coefficient of variation (CV)3.837708239
Kurtosis36.87226721
Mean0.03254581778
Median Absolute Deviation (MAD)0.001977138611
Skewness5.738901412
Sum3.710223227
Variance0.01560034799
MonotonicityNot monotonic
2022-10-06T20:30:31.044284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0023377856651
 
0.8%
0.0059288815241
 
0.8%
0.0028275921371
 
0.8%
0.00047240564261
 
0.8%
0.002264755711
 
0.8%
0.00033759268541
 
0.8%
0.0037019172881
 
0.8%
0.0018684530551
 
0.8%
1.537976883 × 10-51
 
0.8%
0.0056909258141
 
0.8%
Other values (104)104
86.7%
(Missing)6
 
5.0%
ValueCountFrequency (%)
01
0.8%
1.506455117 × 10-61
0.8%
8.639613506 × 10-61
0.8%
9.25408415 × 10-61
0.8%
1.475374927 × 10-51
0.8%
1.537976883 × 10-51
0.8%
1.900918776 × 10-51
0.8%
2.378602616 × 10-51
0.8%
3.219058511 × 10-51
0.8%
3.581355022 × 10-51
0.8%
ValueCountFrequency (%)
11
0.8%
0.5958953681
0.8%
0.49816469971
0.8%
0.38130482781
0.8%
0.26057136661
0.8%
0.17716470431
0.8%
0.14583626461
0.8%
0.046961288541
0.8%
0.045753141431
0.8%
0.036331852991
0.8%

Trade And Other Receivables Current
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct114
Distinct (%)100.0%
Missing6
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.03546971005
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:31.288631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0003292986355
Q10.001507965846
median0.004872671616
Q30.02082183189
95-th percentile0.1317670903
Maximum1
Range1
Interquartile range (IQR)0.01931386605

Descriptive statistics

Standard deviation0.1073374792
Coefficient of variation (CV)3.026173009
Kurtosis58.86297919
Mean0.03546971005
Median Absolute Deviation (MAD)0.004327776525
Skewness6.974408346
Sum4.043546945
Variance0.01152133444
MonotonicityNot monotonic
2022-10-06T20:30:31.414290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.024388783541
 
0.8%
0.0040695781271
 
0.8%
0.001899350721
 
0.8%
0.00052778507661
 
0.8%
0.2046502431
 
0.8%
0.0041726462611
 
0.8%
0.031600013041
 
0.8%
0.0033968960041
 
0.8%
01
 
0.8%
0.004931417481
 
0.8%
Other values (104)104
86.7%
(Missing)6
 
5.0%
ValueCountFrequency (%)
01
0.8%
4.821818698 × 10-51
0.8%
7.033098611 × 10-51
0.8%
0.00013217689431
0.8%
0.00022355019321
0.8%
0.00032780958411
0.8%
0.00033010043231
0.8%
0.00034605396641
0.8%
0.00035431035641
0.8%
0.00042682521771
0.8%
ValueCountFrequency (%)
11
0.8%
0.3198048181
0.8%
0.30636363811
0.8%
0.25477735771
0.8%
0.2046502431
0.8%
0.16841794921
0.8%
0.11203201241
0.8%
0.1087682011
0.8%
0.10273371111
0.8%
0.10102853231
0.8%

Current Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04017833472
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:31.554880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0002432434046
Q10.001109817179
median0.004806666377
Q30.0272373012
95-th percentile0.1331891451
Maximum1
Range1
Interquartile range (IQR)0.02612748402

Descriptive statistics

Standard deviation0.1198219282
Coefficient of variation (CV)2.982252227
Kurtosis39.28167477
Mean0.04017833472
Median Absolute Deviation (MAD)0.004259253256
Skewness5.796844695
Sum4.821400167
Variance0.01435729448
MonotonicityNot monotonic
2022-10-06T20:30:31.679817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.020781766711
 
0.8%
0.0027911951291
 
0.8%
0.60917988141
 
0.8%
0.01722279321
 
0.8%
0.0073302030041
 
0.8%
0.047461722461
 
0.8%
0.008851790351
 
0.8%
0.0005716906461
 
0.8%
0.13009442971
 
0.8%
0.0042727807631
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
6.890864389 × 10-51
0.8%
7.294156901 × 10-51
0.8%
9.782299678 × 10-51
0.8%
0.0001033554891
0.8%
0.00010733291981
0.8%
0.0002503965881
0.8%
0.00026006003441
0.8%
0.00027778999571
0.8%
0.00032028485751
0.8%
ValueCountFrequency (%)
11
0.8%
0.60917988141
0.8%
0.4317831261
0.8%
0.33518513861
0.8%
0.26282816491
0.8%
0.19198873691
0.8%
0.13009442971
0.8%
0.12842202131
0.8%
0.10687661541
0.8%
0.10397728241
0.8%

Current Liabilities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03556534239
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:31.804822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0001820141163
Q10.00125435109
median0.003700324022
Q30.016556157
95-th percentile0.1171972328
Maximum1
Range1
Interquartile range (IQR)0.01530180591

Descriptive statistics

Standard deviation0.122370618
Coefficient of variation (CV)3.440726556
Kurtosis40.50963512
Mean0.03556534239
Median Absolute Deviation (MAD)0.003290211012
Skewness6.051137826
Sum4.267841087
Variance0.01497456816
MonotonicityNot monotonic
2022-10-06T20:30:31.929795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.014718990741
 
0.8%
0.002025801051
 
0.8%
11
 
0.8%
0.20074211461
 
0.8%
0.010319731331
 
0.8%
0.026987644591
 
0.8%
0.0098466233121
 
0.8%
0.00073453912561
 
0.8%
0.12501922811
 
0.8%
0.0045746010791
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
1.390080579 × 10-51
0.8%
2.717867766 × 10-51
0.8%
6.58057315 × 10-51
0.8%
0.00012248689061
0.8%
0.00015984462851
0.8%
0.00018318093151
0.8%
0.00025837472181
0.8%
0.00028225466881
0.8%
0.00029174356811
0.8%
ValueCountFrequency (%)
11
0.8%
0.70637665151
0.8%
0.45840787731
0.8%
0.30529191481
0.8%
0.20074211461
0.8%
0.12501922811
0.8%
0.11678554881
0.8%
0.096300157951
0.8%
0.091283073051
0.8%
0.082199975351
0.8%

Other Current Financial Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)87.5%
Missing112
Missing (%)93.3%
Infinite0
Infinite (%)0.0%
Mean0.1493782688
Minimum0
Maximum1
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:32.023522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0004725508579
median0.0009450567656
Q30.09522975958
95-th percentile0.6844048022
Maximum1
Range1
Interquartile range (IQR)0.09475720872

Descriptive statistics

Standard deviation0.3464161217
Coefficient of variation (CV)2.319052995
Kurtosis7.623126586
Mean0.1493782688
Median Absolute Deviation (MAD)0.0009450567656
Skewness2.743069812
Sum1.195026151
Variance0.1200041294
MonotonicityNot monotonic
2022-10-06T20:30:32.117248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
02
 
1.7%
11
 
0.8%
0.0010163438821
 
0.8%
0.094206534471
 
0.8%
0.00087376964961
 
0.8%
0.00063006781051
 
0.8%
0.098299434911
 
0.8%
(Missing)112
93.3%
ValueCountFrequency (%)
02
1.7%
0.00063006781051
0.8%
0.00087376964961
0.8%
0.0010163438821
0.8%
0.094206534471
0.8%
0.098299434911
0.8%
11
0.8%
ValueCountFrequency (%)
11
0.8%
0.098299434911
0.8%
0.094206534471
0.8%
0.0010163438821
0.8%
0.00087376964961
0.8%
0.00063006781051
0.8%
02
1.7%

Other Noncurrent Financial Assets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)90.0%
Missing110
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean0.1215004641
Minimum0
Maximum1
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:32.195355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.613774465 × 10-5
median0.009237223073
Q30.05008004407
95-th percentile0.5947304322
Maximum1
Range1
Interquartile range (IQR)0.05004390632

Descriptive statistics

Standard deviation0.3104194909
Coefficient of variation (CV)2.55488317
Kurtosis9.690304101
Mean0.1215004641
Median Absolute Deviation (MAD)0.009237223073
Skewness3.09782641
Sum1.215004641
Variance0.0963602603
MonotonicityNot monotonic
2022-10-06T20:30:32.289084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
02
 
1.7%
0.04539617511
 
0.8%
11
 
0.8%
0.015837295371
 
0.8%
0.099400960531
 
0.8%
0.051641333721
 
0.8%
6.531251614 × 10-51
 
0.8%
0.0026371507751
 
0.8%
2.641282082 × 10-51
 
0.8%
(Missing)110
91.7%
ValueCountFrequency (%)
02
1.7%
2.641282082 × 10-51
0.8%
6.531251614 × 10-51
0.8%
0.0026371507751
0.8%
0.015837295371
0.8%
0.04539617511
0.8%
0.051641333721
0.8%
0.099400960531
0.8%
11
0.8%
ValueCountFrequency (%)
11
0.8%
0.099400960531
0.8%
0.051641333721
0.8%
0.04539617511
0.8%
0.015837295371
0.8%
0.0026371507751
0.8%
6.531251614 × 10-51
0.8%
2.641282082 × 10-51
0.8%
02
1.7%

Other Current Financial Liabilities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct31
Distinct (%)83.8%
Missing83
Missing (%)69.2%
Infinite0
Infinite (%)0.0%
Mean0.06170165219
Minimum0
Maximum1
Zeros7
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:32.393953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0001671173127
median0.0008864971726
Q30.008446464688
95-th percentile0.3875335744
Maximum1
Range1
Interquartile range (IQR)0.008279347375

Descriptive statistics

Standard deviation0.2005832711
Coefficient of variation (CV)3.250857376
Kurtosis15.6668194
Mean0.06170165219
Median Absolute Deviation (MAD)0.0008864971726
Skewness3.935761778
Sum2.282961131
Variance0.04023364866
MonotonicityNot monotonic
2022-10-06T20:30:32.503660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
07
 
5.8%
0.011426421931
 
0.8%
0.0084464646881
 
0.8%
0.0025078105321
 
0.8%
0.00043905002051
 
0.8%
0.0040066313351
 
0.8%
0.00029343536251
 
0.8%
0.0015322509831
 
0.8%
0.0008544585171
 
0.8%
0.018260162331
 
0.8%
Other values (21)21
 
17.5%
(Missing)83
69.2%
ValueCountFrequency (%)
07
5.8%
5.415909925 × 10-51
 
0.8%
0.00010747488661
 
0.8%
0.00016711731271
 
0.8%
0.00029343536251
 
0.8%
0.00030304887851
 
0.8%
0.00035996343651
 
0.8%
0.00039593794761
 
0.8%
0.00040806940351
 
0.8%
0.00043905002051
 
0.8%
ValueCountFrequency (%)
11
0.8%
0.68857198381
0.8%
0.3122739721
0.8%
0.097592662351
0.8%
0.082368944771
0.8%
0.024239598951
0.8%
0.018260162331
0.8%
0.011426421931
0.8%
0.0096830922561
0.8%
0.0084464646881
0.8%

Other Noncurrent Financial Liabilities
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)71.4%
Missing113
Missing (%)94.2%
Memory size1.9 KiB
0.0
0.05908937506419568
0.944402870097284
0.03059382841044152
1.0

Length

Max length19
Median length3
Mean length9.571428571
Min length3

Characters and Unicode

Total characters67
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)57.1%

Sample

1st row0.05908937506419568
2nd row0.944402870097284
3rd row0.0
4th row0.0
5th row0.03059382841044152

Common Values

ValueCountFrequency (%)
0.03
 
2.5%
0.059089375064195681
 
0.8%
0.9444028700972841
 
0.8%
0.030593828410441521
 
0.8%
1.01
 
0.8%
(Missing)113
94.2%

Length

2022-10-06T20:30:32.613357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T20:30:32.728060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03
42.9%
0.059089375064195681
 
14.3%
0.9444028700972841
 
14.3%
0.030593828410441521
 
14.3%
1.01
 
14.3%

Most occurring characters

ValueCountFrequency (%)
019
28.4%
48
11.9%
.7
 
10.4%
96
 
9.0%
86
 
9.0%
55
 
7.5%
14
 
6.0%
24
 
6.0%
33
 
4.5%
73
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number60
89.6%
Other Punctuation7
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019
31.7%
48
13.3%
96
 
10.0%
86
 
10.0%
55
 
8.3%
14
 
6.7%
24
 
6.7%
33
 
5.0%
73
 
5.0%
62
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common67
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019
28.4%
48
11.9%
.7
 
10.4%
96
 
9.0%
86
 
9.0%
55
 
7.5%
14
 
6.0%
24
 
6.0%
33
 
4.5%
73
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII67
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019
28.4%
48
11.9%
.7
 
10.4%
96
 
9.0%
86
 
9.0%
55
 
7.5%
14
 
6.0%
24
 
6.0%
33
 
4.5%
73
 
4.5%

Cash Flows From Used In Operating Activities
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4448533884
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:32.847739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4330256346
Q10.4379686448
median0.4388926423
Q30.4425309312
95-th percentile0.4855046639
Maximum1
Range1
Interquartile range (IQR)0.004562286407

Descriptive statistics

Standard deviation0.06981590383
Coefficient of variation (CV)0.1569413781
Kurtosis47.21778277
Mean0.4448533884
Median Absolute Deviation (MAD)0.001758903049
Skewness2.216634362
Sum53.38240661
Variance0.004874260427
MonotonicityNot monotonic
2022-10-06T20:30:32.979390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.47108082961
 
0.8%
0.43846584691
 
0.8%
11
 
0.8%
0.33691194441
 
0.8%
0.44030531331
 
0.8%
0.43552758221
 
0.8%
0.43940440821
 
0.8%
0.43841299771
 
0.8%
0.43474819941
 
0.8%
0.43946423981
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
0.33691194441
0.8%
0.40213041981
0.8%
0.40851387691
0.8%
0.42188505161
0.8%
0.43240498551
0.8%
0.43305830031
0.8%
0.4330625111
0.8%
0.43432793031
0.8%
0.43474819941
0.8%
ValueCountFrequency (%)
11
0.8%
0.60322279881
0.8%
0.57208309521
0.8%
0.50513165491
0.8%
0.49697630771
0.8%
0.48669695771
0.8%
0.48544191161
0.8%
0.48370115351
0.8%
0.47108082961
0.8%
0.47003998171
0.8%

Current Loans And Borrowings
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct44
Distinct (%)88.0%
Missing70
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean0.08207909826
Minimum0
Maximum1
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:33.107014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002751714861
median0.01273822319
Q30.0380969617
95-th percentile0.4167789382
Maximum1
Range1
Interquartile range (IQR)0.03534524684

Descriptive statistics

Standard deviation0.1894801356
Coefficient of variation (CV)2.308506545
Kurtosis13.19159004
Mean0.08207909826
Median Absolute Deviation (MAD)0.01218877091
Skewness3.488833097
Sum4.103954913
Variance0.03590272178
MonotonicityNot monotonic
2022-10-06T20:30:33.229719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
06
 
5.0%
0.012667487942
 
1.7%
0.18906225751
 
0.8%
0.049354965181
 
0.8%
0.030671155181
 
0.8%
5.397299925 × 10-51
 
0.8%
0.0061894422821
 
0.8%
0.002216810391
 
0.8%
0.015895429891
 
0.8%
0.73186411591
 
0.8%
Other values (34)34
28.3%
(Missing)70
58.3%
ValueCountFrequency (%)
06
5.0%
5.397299925 × 10-51
 
0.8%
0.0001913265711
 
0.8%
0.00027551786271
 
0.8%
0.00051620013361
 
0.8%
0.00058270444531
 
0.8%
0.002216810391
 
0.8%
0.0024349286981
 
0.8%
0.0037020733511
 
0.8%
0.004338614621
 
0.8%
ValueCountFrequency (%)
11
0.8%
0.73186411591
0.8%
0.42455222011
0.8%
0.40727826041
0.8%
0.24074787581
0.8%
0.22531344121
0.8%
0.18906225751
0.8%
0.17673752961
0.8%
0.12800496571
0.8%
0.10139057351
0.8%

Noncurrent Loans And Borrowings
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct32
Distinct (%)84.2%
Missing82
Missing (%)68.3%
Infinite0
Infinite (%)0.0%
Mean0.03594693533
Minimum0
Maximum1
Zeros7
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:33.352535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0002398739355
median0.003060674887
Q30.01712351579
95-th percentile0.04809807116
Maximum1
Range1
Interquartile range (IQR)0.01688364186

Descriptive statistics

Standard deviation0.1612263608
Coefficient of variation (CV)4.485121174
Kurtosis37.38047708
Mean0.03594693533
Median Absolute Deviation (MAD)0.003060674887
Skewness6.092086897
Sum1.365983543
Variance0.02599393942
MonotonicityNot monotonic
2022-10-06T20:30:33.461916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
07
 
5.8%
0.013981909611
 
0.8%
0.0097873062131
 
0.8%
0.023906091881
 
0.8%
0.0028636484361
 
0.8%
0.0021361771991
 
0.8%
0.00069596982861
 
0.8%
11
 
0.8%
0.029202379311
 
0.8%
0.0036020425641
 
0.8%
Other values (22)22
 
18.3%
(Missing)82
68.3%
ValueCountFrequency (%)
07
5.8%
1.229617175 × 10-51
 
0.8%
0.00011146918251
 
0.8%
0.00020945619851
 
0.8%
0.00033112714651
 
0.8%
0.0003998810581
 
0.8%
0.00053191755021
 
0.8%
0.00069596982861
 
0.8%
0.0012067146171
 
0.8%
0.0019555368741
 
0.8%
ValueCountFrequency (%)
11
0.8%
0.057941673131
0.8%
0.046360964921
0.8%
0.031606339421
0.8%
0.029202379311
0.8%
0.025687730611
0.8%
0.024864477761
0.8%
0.0243763171
0.8%
0.023906091881
0.8%
0.01739822211
0.8%

Profit Loss
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct115
Distinct (%)100.0%
Missing5
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean0.1283646601
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:33.586886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.108917364
Q10.1118985006
median0.1123991797
Q30.1154353758
95-th percentile0.1802187464
Maximum1
Range1
Interquartile range (IQR)0.003536875216

Descriptive statistics

Standard deviation0.08859439175
Coefficient of variation (CV)0.690177434
Kurtosis83.92312646
Mean0.1283646601
Median Absolute Deviation (MAD)0.001243305482
Skewness8.643983438
Sum14.76193592
Variance0.007848966249
MonotonicityNot monotonic
2022-10-06T20:30:33.821207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1335111491
 
0.8%
0.11479007181
 
0.8%
0.11209698611
 
0.8%
0.11192291541
 
0.8%
0.12022658281
 
0.8%
0.1128256781
 
0.8%
0.22910541631
 
0.8%
0.11108189251
 
0.8%
0.11176532631
 
0.8%
0.11270538951
 
0.8%
Other values (105)105
87.5%
(Missing)5
 
4.2%
ValueCountFrequency (%)
01
0.8%
0.086592120711
0.8%
0.10338652571
0.8%
0.10676244061
0.8%
0.1079025371
0.8%
0.10877374941
0.8%
0.10897891311
0.8%
0.10898215811
0.8%
0.10998569171
0.8%
0.11063582171
0.8%
ValueCountFrequency (%)
11
0.8%
0.32063376111
0.8%
0.28218138131
0.8%
0.23306034171
0.8%
0.22910541631
0.8%
0.21445787211
0.8%
0.16554483541
0.8%
0.16223332661
0.8%
0.15358367141
0.8%
0.14421989361
0.8%

Profit Loss Before Taxation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1282005713
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:33.946176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1087052352
Q10.1116151568
median0.1121775229
Q30.115103593
95-th percentile0.1683000922
Maximum1
Range1
Interquartile range (IQR)0.003488436216

Descriptive statistics

Standard deviation0.0874233514
Coefficient of variation (CV)0.6819263794
Kurtosis84.77546091
Mean0.1282005713
Median Absolute Deviation (MAD)0.001293119689
Skewness8.650329572
Sum15.38406855
Variance0.00764284237
MonotonicityNot monotonic
2022-10-06T20:30:34.071149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1366060811
 
0.8%
0.11179731031
 
0.8%
0.25712385251
 
0.8%
01
 
0.8%
0.11063045631
 
0.8%
0.16579984981
 
0.8%
0.11190410711
 
0.8%
0.11165317141
 
0.8%
0.1213536971
 
0.8%
0.11280011611
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
0.086673272681
0.8%
0.10342847031
0.8%
0.10651879471
0.8%
0.10772155111
0.8%
0.10851159511
0.8%
0.10871542681
0.8%
0.1087187321
0.8%
0.10972061651
0.8%
0.11008200081
0.8%
ValueCountFrequency (%)
11
0.8%
0.32069440981
0.8%
0.30097414231
0.8%
0.25712385251
0.8%
0.23101610791
0.8%
0.21580469921
0.8%
0.16579984981
0.8%
0.16334351621
0.8%
0.15844361371
0.8%
0.14679606251
0.8%

Finance Costs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct86
Distinct (%)90.5%
Missing25
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean0.1158501178
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:34.196119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07981295078
Q10.08018685107
median0.08322142348
Q30.1038743085
95-th percentile0.1787223587
Maximum1
Range1
Interquartile range (IQR)0.0236874574

Descriptive statistics

Standard deviation0.1268940399
Coefficient of variation (CV)1.095329398
Kurtosis31.91810853
Mean0.1158501178
Median Absolute Deviation (MAD)0.003408472696
Skewness5.461127093
Sum11.0057612
Variance0.01610209736
MonotonicityNot monotonic
2022-10-06T20:30:34.321089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.079812950789
 
7.5%
0.082277902832
 
1.7%
0.084774510581
 
0.8%
0.08125154951
 
0.8%
0.080166457951
 
0.8%
11
 
0.8%
0.12682249351
 
0.8%
0.081375001981
 
0.8%
0.17129270031
 
0.8%
0.080698589211
 
0.8%
Other values (76)76
63.3%
(Missing)25
 
20.8%
ValueCountFrequency (%)
01
 
0.8%
0.026472220021
 
0.8%
0.069482420541
 
0.8%
0.079335840531
 
0.8%
0.079812950789
7.5%
0.07982087661
 
0.8%
0.0798338671
 
0.8%
0.079840025361
 
0.8%
0.079901410791
 
0.8%
0.079967995561
 
0.8%
ValueCountFrequency (%)
11
0.8%
0.70240152181
0.8%
0.63161607361
0.8%
0.19573689471
0.8%
0.18026472221
0.8%
0.17806134581
0.8%
0.17129270031
0.8%
0.15823433461
0.8%
0.15769200291
0.8%
0.15656981851
0.8%

Revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03895076948
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:34.464463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.311338714 × 10-5
Q10.0007776849359
median0.002974345778
Q30.01246662329
95-th percentile0.1516714365
Maximum1
Range1
Interquartile range (IQR)0.01168893835

Descriptive statistics

Standard deviation0.1354146854
Coefficient of variation (CV)3.476559954
Kurtosis32.51100972
Mean0.03895076948
Median Absolute Deviation (MAD)0.002606766999
Skewness5.492314937
Sum4.674092338
Variance0.01833713701
MonotonicityNot monotonic
2022-10-06T20:30:34.594117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03157413431
 
0.8%
0.0017335285831
 
0.8%
0.83003195051
 
0.8%
0.00031696351871
 
0.8%
0.0027620856441
 
0.8%
0.018719754841
 
0.8%
0.0028056445751
 
0.8%
0.00041973051731
 
0.8%
0.60131439691
 
0.8%
0.0038262528931
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
3.028768444 × 10-51
0.8%
5.373123597 × 10-51
0.8%
6.082951633 × 10-51
0.8%
6.504761146 × 10-51
0.8%
9.292933618 × 10-51
0.8%
9.312307403 × 10-51
0.8%
9.565920932 × 10-51
0.8%
0.00011420765111
0.8%
0.00012531366081
0.8%
ValueCountFrequency (%)
11
0.8%
0.83003195051
0.8%
0.60131439691
0.8%
0.31253958341
0.8%
0.24038597321
0.8%
0.2373033441
0.8%
0.1471644941
0.8%
0.13895606011
0.8%
0.12182348391
0.8%
0.10491530221
0.8%

Net Tangible Assets margin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8064076073
Minimum0
Maximum1
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:34.722002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5409279139
Q10.7640231989
median0.8361046564
Q30.9182242499
95-th percentile0.9855511768
Maximum1
Range1
Interquartile range (IQR)0.154201051

Descriptive statistics

Standard deviation0.1793519507
Coefficient of variation (CV)0.2224085551
Kurtosis8.075046055
Mean0.8064076073
Median Absolute Deviation (MAD)0.07912887005
Skewness-2.486120429
Sum96.76891287
Variance0.03216712223
MonotonicityNot monotonic
2022-10-06T20:30:34.850658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.88584496771
 
0.8%
0.86793717941
 
0.8%
0.85586437881
 
0.8%
01
 
0.8%
0.72491664471
 
0.8%
0.96015111621
 
0.8%
0.76677042731
 
0.8%
0.7573332911
 
0.8%
0.80320021271
 
0.8%
0.79106001021
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
01
0.8%
0.058984214791
0.8%
0.085466831141
0.8%
0.10542382831
0.8%
0.49695484291
0.8%
0.50655847761
0.8%
0.54273683161
0.8%
0.56480664671
0.8%
0.57045018921
0.8%
0.58891141631
0.8%
ValueCountFrequency (%)
11
0.8%
0.99919794491
0.8%
0.99312442461
0.8%
0.9923669841
0.8%
0.98747013331
0.8%
0.98640200341
0.8%
0.98550639641
0.8%
0.98220217541
0.8%
0.98110454441
0.8%
0.97941424151
0.8%

Operating cash flow rati
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01070383915
Minimum-6.925539324
Maximum6.85218546
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)35.0%
Memory size1.9 KiB
2022-10-06T20:30:34.976322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6.925539324
5-th percentile-2.113602455
Q1-0.09313024671
median0.1149948851
Q30.3789389654
95-th percentile1.29331626
Maximum6.85218546
Range13.77772478
Interquartile range (IQR)0.4720692121

Descriptive statistics

Standard deviation1.494481336
Coefficient of variation (CV)139.621057
Kurtosis12.27585654
Mean0.01070383915
Median Absolute Deviation (MAD)0.249995799
Skewness-1.46177868
Sum1.284460698
Variance2.233474462
MonotonicityNot monotonic
2022-10-06T20:30:35.093010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4974451181
 
0.8%
0.037244073121
 
0.8%
0.37963515131
 
0.8%
-0.34148591011
 
0.8%
0.12731509921
 
0.8%
-0.070577621851
 
0.8%
0.07189855931
 
0.8%
0.052895763231
 
0.8%
-0.019472237241
 
0.8%
0.16249770691
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
-6.9255393241
0.8%
-6.551656921
0.8%
-5.981728811
0.8%
-4.9675119971
0.8%
-2.5360157671
0.8%
-2.1148213241
0.8%
-2.1135383041
0.8%
-1.1767638261
0.8%
-0.93001812231
0.8%
-0.88536063661
0.8%
ValueCountFrequency (%)
6.852185461
0.8%
3.8465424521
0.8%
2.6047517721
0.8%
2.4479780111
0.8%
1.4974451181
0.8%
1.3110531131
0.8%
1.2923827411
0.8%
1.1418283781
0.8%
1.0023893741
0.8%
0.97849227251
0.8%

Debt ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct73
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1575618067
Minimum0
Maximum1.909900364
Zeros48
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:35.221665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01088951689
Q30.2022428138
95-th percentile0.7196994547
Maximum1.909900364
Range1.909900364
Interquartile range (IQR)0.2022428138

Descriptive statistics

Standard deviation0.2838436984
Coefficient of variation (CV)1.801475271
Kurtosis12.40882485
Mean0.1575618067
Median Absolute Deviation (MAD)0.01088951689
Skewness2.979009928
Sum18.9074168
Variance0.08056724514
MonotonicityNot monotonic
2022-10-06T20:30:35.351404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
048
40.0%
0.0065579291471
 
0.8%
0.21813985811
 
0.8%
0.0082507839291
 
0.8%
0.64663437411
 
0.8%
0.012703854641
 
0.8%
0.4422099031
 
0.8%
0.097720635381
 
0.8%
0.14555168931
 
0.8%
0.056853644961
 
0.8%
Other values (63)63
52.5%
ValueCountFrequency (%)
048
40.0%
0.0011445417251
 
0.8%
0.001411229661
 
0.8%
0.0018976028461
 
0.8%
0.0023130690481
 
0.8%
0.0057457354561
 
0.8%
0.006538619631
 
0.8%
0.0065579291471
 
0.8%
0.0067022007521
 
0.8%
0.0082507839291
 
0.8%
ValueCountFrequency (%)
1.9099003641
0.8%
1.0132574891
0.8%
0.99489089691
0.8%
0.8325248941
0.8%
0.7915799791
0.8%
0.75271639451
0.8%
0.71796172111
0.8%
0.70721122321
0.8%
0.64663437411
0.8%
0.56932962881
0.8%

NPBT
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.873989236
Minimum-106.8930388
Maximum457.1718966
Zeros1
Zeros (%)0.8%
Negative30
Negative (%)25.0%
Memory size1.9 KiB
2022-10-06T20:30:35.491996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-106.8930388
5-th percentile-1.776556726
Q1-0.0003326952732
median0.05295337154
Q30.1592070333
95-th percentile0.5086277121
Maximum457.1718966
Range564.0649353
Interquartile range (IQR)0.1595397285

Descriptive statistics

Standard deviation42.96739831
Coefficient of variation (CV)14.95043815
Kurtosis107.7475348
Mean2.873989236
Median Absolute Deviation (MAD)0.08566296224
Skewness9.957861146
Sum344.8787083
Variance1846.197318
MonotonicityNot monotonic
2022-10-06T20:30:35.601345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.24023680651
 
0.8%
0.031510124421
 
0.8%
0.053212605371
 
0.8%
-106.89303881
 
0.8%
-0.10845868061
 
0.8%
0.87858900741
 
0.8%
0.031023757781
 
0.8%
0.025899181221
 
0.8%
0.0049149712761
 
0.8%
0.093831511251
 
0.8%
Other values (110)110
91.7%
ValueCountFrequency (%)
-106.89303881
0.8%
-6.2776407381
0.8%
-6.0563723781
0.8%
-4.6082939411
0.8%
-4.4711909971
0.8%
-2.8750154531
0.8%
-1.7187431091
0.8%
-0.94820565031
0.8%
-0.84121182611
0.8%
-0.62275342851
0.8%
ValueCountFrequency (%)
457.17189661
0.8%
10.650458741
0.8%
2.7853399191
0.8%
0.87858900741
0.8%
0.71765197991
0.8%
0.60491518671
0.8%
0.50355995031
0.8%
0.47798133981
0.8%
0.45856770461
0.8%
0.41966237331
0.8%

Quick Ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct116
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.294140607
Minimum0
Maximum50.20751822
Zeros5
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-10-06T20:30:35.726316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03423249864
Q10.7448783349
median1.311223277
Q32.291615687
95-th percentile5.332041677
Maximum50.20751822
Range50.20751822
Interquartile range (IQR)1.546737352

Descriptive statistics

Standard deviation4.965533856
Coefficient of variation (CV)2.164441813
Kurtosis74.13619758
Mean2.294140607
Median Absolute Deviation (MAD)0.6639563313
Skewness7.940017502
Sum275.2968729
Variance24.65652648
MonotonicityNot monotonic
2022-10-06T20:30:35.851286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
4.2%
1.247085051
 
0.8%
1.3776704191
 
0.8%
0.25171805631
 
0.8%
0.72034466681
 
0.8%
1.1806434971
 
0.8%
0.67583607061
 
0.8%
11.642829131
 
0.8%
2.3072730981
 
0.8%
0.034897757991
 
0.8%
Other values (106)106
88.3%
ValueCountFrequency (%)
05
4.2%
0.021592570931
 
0.8%
0.034897757991
 
0.8%
0.051832012961
 
0.8%
0.098087124251
 
0.8%
0.099904580391
 
0.8%
0.10380161851
 
0.8%
0.18659581041
 
0.8%
0.20442758221
 
0.8%
0.25171805631
 
0.8%
ValueCountFrequency (%)
50.207518221
0.8%
13.769760431
0.8%
12.340614951
0.8%
11.642829131
0.8%
10.703844891
0.8%
5.4295529121
0.8%
5.3269095071
0.8%
5.0268852861
0.8%
4.7828416351
0.8%
4.7503938291
0.8%

Interest coverage ratio
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.7674943
Minimum-237.2366375
Maximum28395.45272
Zeros34
Zeros (%)28.3%
Negative23
Negative (%)19.2%
Memory size1.9 KiB
2022-10-06T20:30:35.976257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-237.2366375
5-th percentile-55.64037164
Q10
median0.3048579559
Q315.2429575
95-th percentile248.693951
Maximum28395.45272
Range28632.68936
Interquartile range (IQR)15.2429575

Descriptive statistics

Standard deviation3038.783107
Coefficient of variation (CV)7.239205389
Kurtosis69.38298905
Mean419.7674943
Median Absolute Deviation (MAD)6.716536306
Skewness8.203201268
Sum50372.09931
Variance9234202.769
MonotonicityNot monotonic
2022-10-06T20:30:36.210576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
28.3%
238.29073481
 
0.8%
-26.415370121
 
0.8%
-0.098271083241
 
0.8%
34.987422091
 
0.8%
7.4814814811
 
0.8%
-112.33809771
 
0.8%
-29.892385991
 
0.8%
28.023046271
 
0.8%
1.9131742151
 
0.8%
Other values (77)77
64.2%
ValueCountFrequency (%)
-237.23663751
0.8%
-112.33809771
0.8%
-110.33703281
0.8%
-105.90638931
0.8%
-58.669291341
0.8%
-55.700639541
0.8%
-55.637199641
0.8%
-53.198677621
0.8%
-39.246773021
0.8%
-37.045700851
0.8%
ValueCountFrequency (%)
28395.452721
0.8%
17573.393151
0.8%
16381
0.8%
946.23502651
0.8%
919.3334621
0.8%
446.35505871
0.8%
238.29073481
0.8%
190.89872071
0.8%
113.50858431
0.8%
94.170890061
0.8%

Risk_bool
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0
100 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Length

2022-10-06T20:30:36.319926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T20:30:36.420054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Most occurring characters

ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0100
83.3%
120
 
16.7%

Interactions

2022-10-06T20:30:25.496776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:37.042517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:39.131762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:41.403541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:43.587468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:45.702234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:47.965903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.165385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.405521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.493936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:56.709486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:58.994373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.292294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.429469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:05.691437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:07.877048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.158038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.239427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.449657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:16.695682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:18.940967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:21.148201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:23.311632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:25.585225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:37.146213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:39.231495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:41.479489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:43.682190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:45.901910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:48.059778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.268179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.491259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.583694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:56.799245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:59.086160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.396871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.524939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:05.780353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:07.963889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.234389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.342200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.539417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:16.787470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:19.033752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:21.234970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:23.391594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:25.682426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:37.239995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:39.326279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:41.581984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:43.772735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:46.001401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:48.150040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.361470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.587003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.674454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:56.896984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:59.181905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.476848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.623886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:05.873703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:08.053149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.322293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.432957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.631171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:16.876231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:19.128503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:21.325694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:23.486410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:25.779975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:37.329755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:39.428179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:41.657393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:43.860407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:46.104862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:48.247378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.453257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.676796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.885853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:56.998678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:59.273663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.566899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.717617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:05.955407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:08.134035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.427799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.518728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.723924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:16.968982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:19.223245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:21.429506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:23.578519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:25.875385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:37.409743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:39.508785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:41.753671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:43.943795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:46.186494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:48.346723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.544016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.767555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.985587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:57.101436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:59.382030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.664059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.923060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:06.038702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:08.240373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.517558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.606493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.816676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:17.060739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-10-06T20:29:45.603297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:47.888525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:50.085592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:52.314679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:54.398192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:56.622716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:29:58.891680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:01.195552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:03.335370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:05.601314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:07.776366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:10.067043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:12.165853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:14.359897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:16.600971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:18.728570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:21.052458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:23.224833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-06T20:30:25.421261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-06T20:30:36.528763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-06T20:30:36.868820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-06T20:30:37.206916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-06T20:30:37.517749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-06T20:30:37.673962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-06T20:30:27.852507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-06T20:30:28.684903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-06T20:30:29.007049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-06T20:30:29.280285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Financial YearDescription Of Presentation CurrencyAssetsGoodwillIntangible Assets Other Than GoodwillLiabilitiesCash And Bank BalancesTrade And Other Receivables CurrentCurrent AssetsCurrent LiabilitiesOther Current Financial AssetsOther Noncurrent Financial AssetsOther Current Financial LiabilitiesOther Noncurrent Financial LiabilitiesCash Flows From Used In Operating ActivitiesCurrent Loans And BorrowingsNoncurrent Loans And BorrowingsProfit LossProfit Loss Before TaxationFinance CostsRevenueNet Tangible Assets marginOperating cash flow ratiDebt ratioNPBTQuick RatioInterest coverage ratioRisk_bool
02019SGD0.0037070.255638NaN0.0073360.0023380.0243890.0207820.014719NaNNaNNaNNaN0.4710810.007778NaN0.1335110.1366060.0847750.0315740.8858451.4974450.0086460.2402371.247085238.2907350
12019SGD0.000445NaNNaN0.0010450.0013890.0039700.0027910.002026NaNNaNNaNNaN0.438466NaNNaN0.1120580.1117970.0814750.0017340.8679370.0372440.0000000.0315101.6352995.1227060
22019SGD0.001736NaNNaN0.0061620.0000090.0121000.0093880.012075NaNNaN0.004966NaN0.438356NaNNaN0.1120560.1116720.0798130.0133390.8005710.0002460.0310450.0012390.7158010.0000000
32019USD0.003921NaNNaN0.0080760.0025280.0169000.0214290.0152810.000000NaNNaN0.0590890.444968NaNNaN0.1177400.1189540.0879290.0165060.8849590.2916570.0134050.1349180.85616442.7695310
42019USD0.040174NaN0.0882630.0348440.3813050.0861240.1919890.078456NaN0.0453960.097593NaN0.456003NaNNaN0.1535840.1584440.1085520.0946960.9487180.1519770.0264780.1501012.72463777.0901820
52019USD0.000952NaNNaN0.0023300.0041560.0093500.0072400.005138NaNNaN0.000886NaN0.4386190.041720NaN0.1125590.112403NaN0.0063210.8627190.0348640.1895940.0377281.6106990.0000000
62019USD0.0093621.000000NaN0.0104630.0016690.1010290.0723550.023701NaNNaN0.000000NaN0.433063NaNNaN0.1158680.1160120.1000400.0430510.932814-0.1504830.0000000.0309843.07787710.2789970
72019SGD0.024124NaN0.0845930.0166950.1771650.0749350.1068770.033235NaNNaN0.000940NaN0.4534590.0213040.0019560.1389150.1416140.0872470.0519120.9562190.3067730.0108780.1753983.737866190.8987210
82019SGD0.002750NaNNaN0.0004120.0000150.0247270.0144920.000298NaNNaNNaNNaN0.438367NaNNaN0.1112450.110994NaN0.0007810.9931240.0303030.000000-0.24252550.2075180.0000000
92019SGD0.000120NaNNaN0.0003240.0011310.0004330.0006470.000550NaNNaNNaNNaN0.439022NaNNaN0.1121580.1119480.0804190.0009920.8469400.7484090.0000000.1012281.26033025.8101670

Last rows

Financial YearDescription Of Presentation CurrencyAssetsGoodwillIntangible Assets Other Than GoodwillLiabilitiesCash And Bank BalancesTrade And Other Receivables CurrentCurrent AssetsCurrent LiabilitiesOther Current Financial AssetsOther Noncurrent Financial AssetsOther Current Financial LiabilitiesOther Noncurrent Financial LiabilitiesCash Flows From Used In Operating ActivitiesCurrent Loans And BorrowingsNoncurrent Loans And BorrowingsProfit LossProfit Loss Before TaxationFinance CostsRevenueNet Tangible Assets marginOperating cash flow ratiDebt ratioNPBTQuick RatioInterest coverage ratioRisk_bool
1102018SGD0.000122NaNNaN0.000961NaNNaN0.0005900.001431NaNNaNNaNNaN0.438156NaNNaNNaN0.1100820.0848250.0011060.570450-0.0889160.000000-0.4212580.000000-14.4948741
1112018SGD0.000029NaNNaN0.000576NaNNaN0.0002780.001258NaNNaNNaNNaN0.438091NaNNaNNaN0.1112590.0805360.0006920.058984-0.1340430.000000-0.1571110.000000-23.4331421
1122017SGD0.000034NaNNaN0.000154NaNNaN0.0002600.000292NaNNaNNaNNaN0.438248NaNNaNNaN0.1115510.0793360.0001140.756618-0.2001800.000000-0.1758570.0000006.5613071
1132017USD0.000139NaNNaN0.0022820.0001180.0008830.0005510.002255NaNNaNNaNNaN0.434328NaNNaN0.1079030.107722NaN0.0004110.105424-1.1767640.000000-2.8750150.2953490.0000001
1142018USD0.002074NaNNaN0.0096450.007087NaN0.0176230.000768NaNNaNNaNNaN0.4385930.0148670.0243760.1110650.1107970.1582340.0004000.7381610.1981421.013257-0.6227533.432438-0.4948421
1152017SGD0.000014NaNNaN0.000152NaNNaN0.0000690.000313NaNNaNNaNNaN0.438480NaNNaNNaN0.1115350.0804770.0003510.5427370.2362560.000000-0.0708640.000000-5.8496111
1162017SGD0.000008NaNNaN0.000062NaNNaN0.0001070.000122NaNNaNNaNNaN0.438361NaNNaNNaN0.1116590.0800340.0002420.6803460.0385800.0000000.0526940.0000008.9986741
1172018USD0.000189NaNNaN0.0006870.0002060.0000700.0001030.001243NaNNaNNaNNaN0.438599NaNNaN0.1119150.1116510.0822270.0000960.7959220.1292370.0000000.1079020.1038020.6665441
1182018SGD0.000051NaNNaN0.0001920.0000190.0008360.0004910.000437NaNNaN0.004062NaN0.437897NaNNaN0.1119000.111631NaN0.0000930.793510-0.6235910.7527160.0436711.2381430.0000001
1192019USD0.000120NaNNaN0.0009050.0000000.0018440.0010760.001601NaNNaNNaNNaN0.4385130.0024350.0005320.1113600.1110910.0807760.0000930.5889110.0659790.428223-1.7187430.799835-25.8505281